Yoon Seowon, Jang Jihee, Son Gaeun, Park Soohyun, Hwang Jueun, Choeh Joon Yeon, Choi Kee-Hong
School of Psychology, Korea University, Seoul, Republic of Korea.
KU Mind Health Institute, Korea University, Seoul, Republic of Korea.
Front Psychiatry. 2024 Aug 1;15:1437569. doi: 10.3389/fpsyt.2024.1437569. eCollection 2024.
With rapid advancements in natural language processing (NLP), predicting personality using this technology has become a significant research interest. In personality prediction, exploring appropriate questions that elicit natural language is particularly important because questions determine the context of responses. This study aimed to predict levels of neuroticism-a core psychological trait known to predict various psychological outcomes-using responses to a series of open-ended questions developed based on the five-factor model of personality. This study examined the model's accuracy and explored the influence of item content in predicting neuroticism.
A total of 425 Korean adults were recruited and responded to 18 open-ended questions about their personalities, along with the measurement of the Five-Factor Model traits. In total, 30,576 Korean sentences were collected. To develop the prediction models, the pre-trained language model KoBERT was used. Accuracy, F1 Score, Precision, and Recall were calculated as evaluation metrics.
The results showed that items inquiring about social comparison, unintended harm, and negative feelings performed better in predicting neuroticism than other items. For predicting depressivity, items related to negative feelings, social comparison, and emotions showed superior performance. For dependency, items related to unintended harm, social dominance, and negative feelings were the most predictive.
We identified items that performed better at neuroticism prediction than others. Prediction models developed based on open-ended questions that theoretically aligned with neuroticism exhibited superior predictive performance.
随着自然语言处理（NLP）的迅速发展，利用这项技术预测人格已成为一项重要的研究兴趣点。在人格预测中，探索能引出自然语言的恰当问题尤为重要，因为问题决定了回答的背景。本研究旨在通过对基于人格五因素模型开发的一系列开放式问题的回答来预测神经质水平——一种已知能预测各种心理结果的核心心理特质。本研究检验了该模型的准确性，并探讨了项目内容在预测神经质方面的影响。
共招募了425名韩国成年人，他们回答了18个关于其人格的开放式问题，同时还进行了五因素模型特质的测量。总共收集了30576个韩语句子。为了开发预测模型，使用了预训练语言模型KoBERT。计算了准确率、F1分数、精确率和召回率作为评估指标。
结果表明，询问社会比较、意外伤害和负面情绪的项目在预测神经质方面比其他项目表现更好。对于预测抑郁性，与负面情绪、社会比较和情绪相关的项目表现更优。对于依赖性，与意外伤害、社会支配和负面情绪相关的项目预测性最强。
我们确定了在预测神经质方面比其他项目表现更好的项目。基于在理论上与神经质相符的开放式问题开发的预测模型表现出了卓越的预测性能。
